import  matplotlib.pyplot as plt
from  sklearn.decomposition import PCA
from  sklearn.datasets import  load_iris
data = load_iris()
y =data.target
x=data.data
pca=PCA(n_components=2)
reduced_x=pca.fit_transform(x)
red_x,red_y=[],[]
bule_x,bule_y=[],[]
green_x,green_y=[],[]
for i in range(len(reduced_x)):
    if y[i] == 0:
        red_x.append(reduced_x[i][0])
        red_y.append(reduced_x[i][1])
    elif y[i] == 1:
        bule_x.append(reduced_x[i][0])
        bule_y.append(reduced_x[i][1])
    else:
        green_x.append(reduced_x[i][0])
        green_y.append(reduced_x[i][1])
plt.scatter(red_x,red_y,c='r',marker='x')
plt.scatter(bule_x,bule_y,c='b',marker='D')
plt.scatter(green_x,green_y,c='g',marker='.')
plt.show